feature interaction
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2022 ◽  
Vol 302 ◽  
pp. 103589
Author(s):  
Yifan Chen ◽  
Yang Wang ◽  
Pengjie Ren ◽  
Meng Wang ◽  
Maarten de Rijke

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Shiyong Hu ◽  
Jia Yan ◽  
Dexiang Deng

Low-light image enhancement has been gradually becoming a hot research topic in recent years due to its wide usage as an important pre-processing step in computer vision tasks. Although numerous methods have achieved promising results, some of them still generate results with detail loss and local distortion. In this paper, we propose an improved generative adversarial network based on contextual information. Specifically, residual dense blocks are adopted in the generator to promote hierarchical feature interaction across multiple layers and enhance features at multiple depths in the network. Then, an attention module integrating multi-scale contextual information is introduced to refine and highlight discriminative features. A hybrid loss function containing perceptual and color component is utilized in the training phase to ensure the overall visual quality. Qualitative and quantitative experimental results on several benchmark datasets demonstrate that our model achieves relatively good results and has good generalization capacity compared to other state-of-the-art low-light enhancement algorithms.


Author(s):  
Kalaivaani P C D ◽  
V. E. Sathishkumar ◽  
Wesam Atef Hatamleh ◽  
Kamel Dine Haouam ◽  
B. Venkatesh ◽  
...  

2021 ◽  
Author(s):  
Rutian Qing ◽  
Yizhi Liu ◽  
Yijiang Zhao ◽  
Zhuhua Liao ◽  
YuXuan Liu

Author(s):  
Kalaivani P C D ◽  
Sathishkumar V E ◽  
Wesam Atef Hatamleh ◽  
Kamel Dine Haouam ◽  
B. Venkatesh ◽  
...  

2021 ◽  
pp. 397-407
Author(s):  
Dengtai Tan ◽  
Changpeng He ◽  
Yiqun Wang

2021 ◽  
Vol 11 (21) ◽  
pp. 10502
Author(s):  
Ling Dai ◽  
Guangyun Zhang ◽  
Jinqi Gong ◽  
Rongting Zhang

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.


Medicines ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 66
Author(s):  
Charat Thongprayoon ◽  
Caroline C. Jadlowiec ◽  
Napat Leeaphorn ◽  
Jackrapong Bruminhent ◽  
Prakrati C. Acharya ◽  
...  

Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States.


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